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Title: Network Scale Ubiquitous Volume Estimation Using Tree-Based Ensemble Learning Methods

Abstract

Currently real-time, ubiquitous volume data for roadway networks remains the key missing dimension in traffic operations. Most volume data are average annual daily traffic (AADT) measures derived from the Highway Performance Monitoring System (HPMS). Although methods to factor the AADT to hourly averages for typical day of week exist, actual volume data is limited to a sparse collection of locations in which volumes are continuously recorded. This paper explores the use of state-of-art machine learning techniques to estimate accurate real-time volume measures that span the highway network providing ubiquitous coverage in space, and point-in-time measures for a specific date and time. Three tree-based ensemble learning models, random forest (RF), gradient boost machine (GBM), and extreme gradient boost (XGBoost), were tested for volume estimation by learning from combined dataset of commercial probe data provided by TomTom, the FHWA's Travel Monitoring Analysis System (TMAS) data, and other infrastructure attributes such as number of lanes, speed limit, and weather. The methods were tested on major corridors and freeways in the metropolitan area of Denver. All three machine learning methods were able to provide hourly volume estimates 24 hours a day, 7 days a week, and 365 days a year with around 18% meanmore » absolute error to true volume and about 5% of error with respect to roadway capacity. The low error measures allow the potential application by transportation agencies.« less

Authors:
ORCiD logo [1]; ORCiD logo [1];  [2];  [3]
  1. National Renewable Energy Laboratory (NREL), Golden, CO (United States)
  2. University of Utah
  3. TomTom
Publication Date:
Research Org.:
National Renewable Energy Lab. (NREL), Golden, CO (United States)
Sponsoring Org.:
USDOE Office of Energy Efficiency and Renewable Energy (EERE), Vehicle Technologies Office (EE-3V)
OSTI Identifier:
1558882
Report Number(s):
NREL/CP-5400-70896
DOE Contract Number:  
AC36-08GO28308
Resource Type:
Conference
Resource Relation:
Conference: Presented at the Transportation Research Board 97th Annual Meeting, 7-11 January 2018, Washington, D.C.
Country of Publication:
United States
Language:
English
Subject:
33 ADVANCED PROPULSION SYSTEMS; tree-based ensemble learning methods; tree-based ensemble learning models; traffic estimation; TomTom probe data; random forest (RF); gradient boost machine (GBM); extreme gradient boost (XGBoost)

Citation Formats

Hou, Yi, Young, Stanley E, Dimri, Anuj, and Cohn, Nicholas. Network Scale Ubiquitous Volume Estimation Using Tree-Based Ensemble Learning Methods. United States: N. p., 2018. Web.
Hou, Yi, Young, Stanley E, Dimri, Anuj, & Cohn, Nicholas. Network Scale Ubiquitous Volume Estimation Using Tree-Based Ensemble Learning Methods. United States.
Hou, Yi, Young, Stanley E, Dimri, Anuj, and Cohn, Nicholas. Thu . "Network Scale Ubiquitous Volume Estimation Using Tree-Based Ensemble Learning Methods". United States.
@article{osti_1558882,
title = {Network Scale Ubiquitous Volume Estimation Using Tree-Based Ensemble Learning Methods},
author = {Hou, Yi and Young, Stanley E and Dimri, Anuj and Cohn, Nicholas},
abstractNote = {Currently real-time, ubiquitous volume data for roadway networks remains the key missing dimension in traffic operations. Most volume data are average annual daily traffic (AADT) measures derived from the Highway Performance Monitoring System (HPMS). Although methods to factor the AADT to hourly averages for typical day of week exist, actual volume data is limited to a sparse collection of locations in which volumes are continuously recorded. This paper explores the use of state-of-art machine learning techniques to estimate accurate real-time volume measures that span the highway network providing ubiquitous coverage in space, and point-in-time measures for a specific date and time. Three tree-based ensemble learning models, random forest (RF), gradient boost machine (GBM), and extreme gradient boost (XGBoost), were tested for volume estimation by learning from combined dataset of commercial probe data provided by TomTom, the FHWA's Travel Monitoring Analysis System (TMAS) data, and other infrastructure attributes such as number of lanes, speed limit, and weather. The methods were tested on major corridors and freeways in the metropolitan area of Denver. All three machine learning methods were able to provide hourly volume estimates 24 hours a day, 7 days a week, and 365 days a year with around 18% mean absolute error to true volume and about 5% of error with respect to roadway capacity. The low error measures allow the potential application by transportation agencies.},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = {2018},
month = {1}
}

Conference:
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